Using Vector Embeddings For Sentiment Analysis
Neural Network Layers
- Neural networks usually have three types of layers:
- Input Layer
- Hidden layers
- Output layer
What are embeddings?
- Embeddings are a technique that allow us to map words or phrases into a corresponding vector of real numbers, where the position and direction of the vector capture the word’s semantic meaning in relation to other words.
- They make high-dimensional data like words readable to our algorithm/model and allows our model to recognize and learn meaningful relationships and similarities between words
Dense Layer & Cosine Similarity
- Cosine Similarity
- Measures the cosine of the angle between two non-zero vectors, providing a measure of similarity.
- The smaller the angle the higher the similarity between the two vectors.
- \(cosine\_similarity(u,v) = \frac{u.v}{||u|| ||v||}\)
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- Dense Layer
- A logistic regression model with a sigmoid activation function used for binary classification.
- It outputs the probability that the input belongs to a positive class.
- \(y=\sigma(W⋅z+b)\)
- Where:
- z is the flattened input vector.
- W is the weight vector.
- b is the bias term.
- \(\sigma(x) = \frac{1}{1+e^{-x}}\) is the sigmoid function.